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Vector Norms

Before we move on in discussing more about the errors in computed solutions $\hat{x}$ to the actual solution $x$ of a system of equations $Ax = b$ (where $A$ is an $n \times n$ matrix), we will first need to define what the norm of a vector is and look at some important norms. We will only be looking at norms on $\mathbb{R}^n$, that is the set $n$-component vectors with real components, or equivalently, the set of $n \times 1$ matrices whose entries are real.

Definition: A Vector Norm on $\mathbb{R}^n$ is a function that maps each vector $x \in \mathbb{R}^n$ to a number $\| x \| \in \mathbb{R}$ that has the following properties:1)$\| x \| ≥ 0$ for all $x \in \mathbb{R}$ and $\| x \| = 0$ if and only if $x = 0$ (Positivity and Definiteness Property).2)$\| \alpha x \| = \mid \alpha \mid \| x \|$ for all $\alpha \in \mathbb{R}$ and for all $x \in \mathbb{R}^n$.3)$\| x + y \| ≤ \| x \| + \| y \|$ for all $x, y \in \mathbb{R}^n$ (The Triangle Inequality for Vector Norms).

There are many different types of vector norms that can be defined on $\mathbb{R}^n$. We will only be interested in the following three vector norms for right now. Let $x = (x_1, x_2, ..., x_n) \in \mathbb{R}^n$. Then:

We will verify that $\| x \|_1$ is indeed a norm on $\mathbb{R}^n$ by verifying the three properties that define a norm. The reader should verify that the functions $\| x \|_2$ and $\| x \|_{\infty}$ are also norms on $\mathbb{R}^n$.